{"id":"W2934511613","doi":"10.1155/2019/4805613","title":"A Two-Step Approach for Airborne Delay Minimization Using Pretactical Conflict Resolution in Free-Route Airspace","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Air Traffic Management and Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Anadolu Üniversitesi","keywords":"Solver; Tabu search; Fuel efficiency; Minification; Mathematical optimization; Computer science; Resolution (logic); Metaheuristic; Algorithm; Engineering; Mathematics; Automotive engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005962444,0.001174911,0.0008358104,0.0007475349,0.0005409273,0.0009470144,0.001636886,0.001305604,0.003261636],"category_scores_gemma":[0.0007188411,0.0004948805,0.001175558,0.0005845081,0.0003043143,0.0008885061,0.0009759793,0.001129653,0.0003805112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005610636,"about_ca_system_score_gemma":0.001770825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003692232,"about_ca_topic_score_gemma":0.003781347,"domain_scores_codex":[0.9995337,0.0001514896,0.00001762025,0.00007533241,0.0001536079,0.0000683032],"domain_scores_gemma":[0.9997614,0.00009085592,0.00003547753,0.00001879176,0.00006754582,0.00002584743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005634527,0.0001177321,0.0003160486,0.0001172679,0.00004153463,0.00008402164,0.00007012716,0.9481612,0.003250392,0.008703997,0.0007664107,0.03831501],"study_design_scores_gemma":[0.00001580481,0.0001343109,0.0001000764,0.00000908114,0.00001232032,0.00003906828,0.0000337337,0.9955822,0.00107579,0.001988378,0.001000053,0.000009195899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01727811,0.0001900117,0.9771021,0.0001118811,0.0000379934,0.0001498218,0.00004366591,0.0001381798,0.004948304],"genre_scores_gemma":[0.4402015,0.0002914098,0.5515326,0.0001424393,0.000032981,0.0005037436,0.0001633718,0.000077039,0.007054895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003692232,"threshold_uncertainty_score":0.01091123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01176664080879339,"score_gpt":0.2396812833408985,"score_spread":0.2279146425321051,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}